Satellite retrievals of carbon monoxide (CO) are routinely assimilated in atmospheric chemistry models to improve air quality forecasts, produce reanalyzes and to estimate emissions. This study applies the quantile‐conserving ensemble filter framework, a novel assimilation algorithm that can deal with non‐Gaussian and modestly nonlinear distributions. Instead of assuming normal distributions like the Ensemble Adjustments Kalman Filter (EAKF), we now apply a bounded normal rank histogram (BNRH) distribution for the prior. The goal is to efficiently estimate bounded quantities such as CO atmospheric mixing ratios and emission fluxes while maintaining the good performance achieved by the EAKF. We contrast assimilating meteorological and MOPITT (Measurement of Pollution in the Troposphere) observations for May 2018. We evaluate the results with the fourth deployment of the NASA Atmospheric Tomography Mission (ATom‐4) airborne field campaign. We also compare simulations with CO tropospheric columns from the network for the detection of atmospheric composition change and surface in‐situ observations from NOAA carbon cycle greenhouse gases. While the differences remain small, the BNRH approach clearly works better than the EAKF in comparison to all observation data sets.

The number of greenhouse gas (GHG) observing satellites has greatly expanded in recent years, and these new datasets provide an unprecedented constraint on global GHG sources and sinks. However, a continuing challenge for inverse models that are used to estimate these sources and sinks is the sheer number of satellite observations, sometimes in the millions per day. These massive datasets often make it prohibitive to implement inverse modeling calculations and/or assimilate the observations using many types of atmospheric models. Although these satellite datasets are very large, the information content of any single observation is often modest and non-exclusive due to redundancy with neighboring observations and due to measurement noise. In this study, we develop an adaptive approach to reduce the size of satellite datasets using geostatistics. A guiding principle is to reduce the data more in regions with little variability in the observations and less in regions with high variability. We subsequently tune and evaluate the approach using synthetic and real data case studies for North America from NASA’s Orbiting Carbon Observatory-2 (OCO-2) satellite. The proposed approach to data reduction yields more accurate CO2 flux estimates than the commonly used method of binning and averaging the satellite data. We further develop a metric for choosing a level of data reduction; we can reduce the satellite dataset to an average of one observation per ∼ 80–140 km for the specific case studies here without substantially compromising the flux estimate, but we find that reducing the data further quickly degrades the accuracy of the estimated fluxes. Overall, the approach developed here could be applied to a range of inverse problems that use very large trace gas datasets.

Geostatistical inverse modeling (GIM) has become a common approach to estimating greenhouse gas fluxes at the Earth's surface using atmospheric observations. GIMs are unique relative to other commonly used approaches because they do not require a single emissions inventory or a bottom–up model to serve as an initial guess of the fluxes. Instead, a modeler can incorporate a wide range of environmental, economic, and/or land use data to estimate the fluxes. Traditionally, GIMs have been paired with in situ observations that number in the thousands or tens of thousands. However, the number of available atmospheric greenhouse gas observations has been increasing enormously as the number of satellites, airborne measurement campaigns, and in situ monitoring stations continues to increase. This era of prolific greenhouse gas observations presents computational and statistical challenges for inverse modeling frameworks that have traditionally been paired with a limited number of in situ monitoring sites. In this article, we discuss the challenges of estimating greenhouse gas fluxes using large atmospheric datasets with a particular focus on GIMs. We subsequently discuss several strategies for estimating the fluxes and quantifying uncertainties, strategies that are adapted from hydrology, applied math, or other academic fields and are compatible with a wide variety of atmospheric models. We further evaluate the accuracy and computational burden of each strategy using a synthetic CO2 case study based upon NASA's Orbiting Carbon Observatory 2 (OCO-2) satellite. Specifically, we simultaneously estimate a full year of 3-hourly CO2 fluxes across North America in one case study – a total of 9.4×106 unknown fluxes using 9.9×104 synthetic observations. The strategies discussed here provide accurate estimates of CO2 fluxes that are comparable to fluxes calculated directly or analytically. We are also able to approximate posterior uncertainties in the fluxes, but these approximations are, typically, an over- or underestimate depending upon the strategy employed and the degree of approximation required to make the calculations manageable.

Abstract. Geostatistical inverse modeling (GIM) has become a common approach to estimating greenhouse gas fluxes at the Earth's surface using atmospheric observations. GIMs are unique relative to other commonly-used approaches because they do not require a single emissions inventory or a bottom-up model to serve as an initial guess of the fluxes. Instead, a modeler can incorporate a wide range of environmental, economic, and/or land use data to estimate the fluxes. Traditionally, GIMs have been paired with in situ observations that number in the thousands or tens of thousands. However, the number of available atmospheric greenhouse gas observations has been increasing enormously as the number of satellites, airborne measurement campaigns, and in situ monitoring stations continues to increase. This era of prolific greenhouse gas observations presents computational and statistical challenges for inverse modeling frameworks that have traditionally been paired with a limited number of in situ monitoring sites. In this article, we discuss the challenges of estimating greenhouse gas fluxes using large atmospheric datasets with a particular focus on GIMs. We subsequently discuss several strategies for estimating the fluxes and quantifying uncertainties, strategies that are adapted from hydrology, applied math, or other academic fields and are compatible with a wide variety of atmospheric models. We further evaluate the accuracy and computational burden of each strategy using CO2 observations from NASA's Orbiting Carbon Observatory 2 (OCO-2) satellite. Specifically, we simultaneously estimate a full year of 3-hourly CO2 fluxes across North America in one case study – a total of 9.4 × 106 unknown fluxes using 9.9 × 104 observations. The strategies discussed here provide accurate estimates of CO2 fluxes that are comparable to fluxes calculated directly or analytically. We are also able to approximate posterior uncertainties in the fluxes, but these approximation are typically an over- or underestimate depending upon the strategy employed and the degree of approximation required to make the calculations manageable.

Long-term atmospheric CO2 mole fraction and δ13CO2 observations over North America document persistent responses to the El Niño-Southern Oscillation. We estimate these responses corresponded to 0.61 (0.45 to 0.79) PgC year-1 more North American carbon uptake during El Niño than during La Niña between 2007 and 2015, partially offsetting increases of net tropical biosphere-to-atmosphere carbon flux around El Niño. Anomalies in derived North American net ecosystem exchange (NEE) display strong but opposite correlations with surface air temperature between seasons, while their correlation with water availability was more constant throughout the year, such that water availability is the dominant control on annual NEE variability over North America. These results suggest that increased water availability and favorable temperature conditions (warmer spring and cooler summer) caused enhanced carbon uptake over North America near and during El Niño.

A wide array of observations have recently emerged that can provide top-down constraints on North American methane (CH4) emissions estimates: measurements made in-situ and from flasks at the surface, on towers, aboard ships, and aboard aircraft as part of the North American Carbon Program; and retrievals of column averaged mole fractions taken from the surface by the Total Carbon Column Observation Network (TCCON), and from space by the Greenhouse Gases Observing Satellite (GOSAT). We use the CarbonTracker-Lagrange CH4 inverse modeling framework to investigate the utility of the different observation types, and to find optimal methods of integrating them. We examine the impact of weighting data using various models of uncertainty constrained by restricted maximum likelihood estimation. We test methods of separating data for independent quantification of boundary values, fluxes, and …

The stratospheric aerosol layer has been monitored with lidars at Mauna Loa Observatory in Hawaii and Boulder in Colorado since 1975 and 2000, respectively. Following the Pinatubo volcanic eruption in June 1991, the global stratosphere has not been perturbed by a major volcanic eruption providing an unprecedented opportunity to study the background aerosol. Since about 2000, an increase of 4–7% per year in the aerosol backscatter in the altitude range 20–30 km has been detected at both Mauna Loa and Boulder. This increase is superimposed on a seasonal cycle with a winter maximum that is modulated by the quasi‐biennial oscillation (QBO) in tropical winds. Of the three major causes for a stratospheric aerosol increase: volcanic emissions to the stratosphere, increased tropical upwelling, and an increase in anthropogenic sulfur gas emissions in the troposphere, it appears that a large increase in coal burning since 2002, mainly in China, is the likely source of sulfur dioxide that ultimately ends up as the sulfate aerosol responsible for the increased backscatter from the stratospheric aerosol layer. The results are consistent with 0.6–0.8% of tropospheric sulfur entering the stratosphere.

An infrared absorption spectrometer has been constructed to measure the stable isotopic composition of atmospheric methane samples. The spectrometer employs periodically poled lithium niobate to generate 15 microW of tunable difference-frequency radiation from two near-infrared diode lasers that probe the nu3 rotational-vibrational band of methane at 3.4 microm. To enhance the signal, methane is extracted from 25 l of air by use of a cryogenic chromatographic column and is expanded into the multipass cell for analysis. A measurement precision of 12 per thousand is demonstrated for both delta13C and deltaD.